1 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
Bin Xu, Ayan Banerjee, Sandeep Gupta
Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recov…
NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-sta…
MedXAI: A Retrieval-Augmented and Self-Verifying Framework for Knowledge-Guided Medical Image Analysis
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly under domain shifts and rare-class conditions. Deep learning models oft…
Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures
Bin Xu, Ayan Banerjee, Sandeep Gupta
Model Recovery (MR) is a core primitive for physical AI and real-time digital twins, but GPUs often execute MR inefficiently due to iterative dependencies, kernel-launch overheads,…
Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Domain generalization remains a critical challenge in medical imaging, where models trained on single sources often fail under real-world distribution shifts. We propose KG-DG, a n…
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM
Payal Kamboj, Ayan Banerjee, Bin Xu +1
Rare events, due to their infrequent occurrences, do not have much data, and hence deep learning techniques fail in estimating the distribution for such data. Open-vocabulary model…